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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Applied Data Sciences International Journal of Robotics and Control Systems Journal of Medicine and Health Technology
Kuswanto, Djoko
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience

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Application of 3D Printing Technology for Medical Purposes: A State of the Art Pamungkas, Yuri; Kuswanto, Djoko; Syafira Eljatin, Dwinka; Nugroho Njoto, Edwin
Journal of Medicine and Health Technology Vol. 2 No. 1 (2025)
Publisher : Direktorat Riset dan Pengabdian Kepada Masyarakat, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j30466865.v2i1.2255

Abstract

Introduction: The application of 3D printing technology in healthcare has revolutionized various medical practices, allowing for personalized solutions tailored to individual patient needs. This study explores the current state of 3D printing in medical applications, highlighting its benefits and challenges. Method: A comprehensive review of existing literature was conducted, focusing on the utilization of 3D printing for creating anatomical models, prosthetics, and bioprinting of living tissues. The analysis included a survey of various additive manufacturing techniques, such as Selective Laser Sintering (SLS) and Stereolithography (SLA), to assess their effectiveness in medical contexts. Results: The findings indicate that 3D printing enhances surgical planning by providing accurate anatomical models, thereby improving surgical outcomes. Additionally, the technology facilitates the production of custom implants and prosthetics, leading to better integration with patients' anatomy. Bioprinting has shown promise in developing artificial organs and regenerative therapies, significantly impacting transplant medicine. Discussion: While 3D printing offers substantial advantages, challenges such as regulatory hurdles, ethical considerations, and the need for standardized practices remain. The technology's potential in personalized medicine is vast, suggesting a future where individualized treatments are commonplace. Continued research and collaboration among medical professionals, technologists, and regulatory bodies are essential to address these challenges and optimize the benefits of 3D printing in healthcare. This study underscores the transformative impact of 3D printing in medicine and its potential to enhance patient care and outcomes across various applications.
Deep Learning Approach to Lung Cancer Detection Using the Hybrid VGG-GAN Architecture Pamungkas, Yuri; Kuswanto, Djoko; Syaifudin, Achmad; Triandini, Evi; Hapsari, Dian Puspita; Nakkliang, Kanittha; Uda, Muhammad Nur Afnan; Hashim, Uda
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1923

Abstract

Lung cancer ranks among the primary contributors to cancer-related deaths globally, highlighting the need for accurate and efficient detection methods to enable early diagnosis. However, deep learning models such as VGG16 and VGG19, commonly used for CT scan image classification, often face challenges related to class imbalance, resulting in classification bias and reduced sensitivity to minority classes. This study contributes by proposing an integration of the VGG architecture and Generative Adversarial Networks (GANs) to improve lung cancer classification performance through balanced and realistic synthetic data augmentation. The proposed approach was evaluated using two datasets: the IQ-OTH/NCCD Dataset, which classifies patients into Benign, Malignant, and Normal categories based on clinical condition, and the Lung Cancer CT Scan Dataset, annotated with histopathological labels: Adenocarcinoma, Squamous Cell Carcinoma, Large Cell Carcinoma, and Normal. The method involves initial training of the VGG model without augmentation, followed by GAN-based data generation to balance class distribution. The experimental results show that, prior to augmentation, the models achieved relatively high overall accuracy, but with poor performance on minority classes (marked by low precision and F1-scores and FPR exceeding 8% in certain cases). After augmentation with GAN, all performance metrics improved dramatically and consistently across all classes, achieving near-perfect precision, TPR, F1-score, and overall accuracy of 99.99%, and FPR sharply reduced to around 0.001%. In conclusion, the integration of GAN and VGG proved effective in overcoming data imbalance and enhancing model generalization, making it a promising solution for AI-based lung cancer diagnostic systems.
Exploring the Determinants of User Acceptance for the Digital Diary Application in Type 1 Diabetes Management: A Structural Equation Modeling Approach Triandini, Evi; Permana, Putu Adi Guna; Hanief, Shofwan; Kuswanto, Djoko; Pamungkas, Yuri; Perwitasari, Rayi Kurnia; Hisbiyah, Yuni; Rochmah, Nur; Faizi, Muhammad
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.767

Abstract

Effective management of Type 1 Diabetes (T1D), especially in children, requires continuous monitoring and care. Digital health applications have become vital in supporting routine T1D management, including insulin delivery, glucose monitoring, nutrition, and physical activity tracking. This study investigates factors influencing user acceptance of a digital diary app designed for children with T1D and their families. Using an extended Technology Acceptance Model incorporating Trust, Perceived Risk, Perceived Enjoyment, and Social Influence, a survey was conducted with 114 participants, including parents, physicians, and dietitians. Data were analyzed using Partial Least Squares Structural Equation Modeling. Findings indicate that perceived usefulness, trust, and social influence significantly affect users' attitudes and intentions to use the app, through the accepted hypothesis that considered path coefficients and p-values. Conversely, hypothesis that shows relation between perceived ease of use, enjoyment, and risk toward intention were rejected, showing unsignificant relations toward user intention to use. Furthermore, this study recommends prioritizing robust security features, fostering user trust, and engaging social networks to enhance digital health adoption in pediatric care. Future research should further explore the roles of perceived risk and enjoyment in sustaining long-term engagement
Cranioplasty Training Innovation Using Design Thinking: AugmentedReality and Interchangeability-Based Mannequin Prototype Kuswanto, Djoko; Alifah Putri, Athirah Hersyadea; Zulaikha, Ellya; Apriawan, Tedy; Pamungkas, Yuri; Triandini, Evi; Jafari, Nadya Paramitha; Chusak, Thassaporn
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.5055

Abstract

Cranioplasty, a surgical procedure to reconstruct the anatomical structure of the human skull, is commonlyperformed in Indonesia due to the malignancy of diseases, traffic accidents, and workplaceinjuries. If left untreated, this condition can lead to serious complications. Although cranioplasty isgenerally considered a relatively easy surgery, it has a fairly high postoperative complication rate ofaround 10.3%. The decreasing availability of cadavers for anatomical studies has significantly limitedtraining opportunities. Therefore, efficient and effective training tools are essential, especially whentraditional resources are insufficient to meet educational needs. Additionally, the training capabilitiesof commercially available mannequins or replicas used in medical institutions remain limited. Themain objective of this project was to develop a smart, modular cranioplasty training mannequin designedfor repeated use, incorporating Augmented Reality (AR) technology to visualize anatomicalstructures that cannot be physically replicated. Using a design thinking approach, data was collectedthrough interviews with neurosurgeons, neurosurgery residents, and cranioplasty specialists, as well asthrough a review of relevant literature. Usability testing of the developed prototype yielded promisingresults, with high ratings for ease of use (4.8), training effectiveness (4.5), anatomical realism (4.3),and material durability (4.5) on a 5-point Likert scale. These findings demonstrated strong user approvaland confirmed the model’s potential to support surgical skill development in a practical andreproducible manner. The resulting AR-integrated training mannequin offers an innovative, engaging,and durable solution to address current challenges in neurosurgical education, especially in resourceconstrainedsettings.
Co-Authors Achmad Syaifudin Alifah Putri, Athirah Hersyadea Chusak, Thassaporn Dian Puspita Hapsari Evi Triandini Hashim, Uda Hisbiyah, Yuni Jafari, Nadya Paramitha Muhammad Faizi, Muhammad Nakkliang, Kanittha Nugroho Njoto, Edwin Nur Rochmah, Nur Perwitasari, Rayi Kurnia Putu Adi Guna Permana Shofwan Hanief Syafira Eljatin, Dwinka Tedy Apriawan Uda, Muhammad Nur Afnan Yuri Pamungkas Zulaikha, Ellya